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1Z0-515 exam Dumps Source : Data Warehousing 11g Essentials

Test Code : 1Z0-515
Test designation : Data Warehousing 11g Essentials
Vendor designation : Oracle
: 70 actual Questions

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Oracle Data Warehousing 11g Essentials

Oracle Database 12c relational database administration system overview | actual Questions and Pass4sure dumps

Oracle Database 12c is a relational database administration device (RDBMS) designed for each on-premises and cloud makes exhaust of and deployable on a choice of clustered or unique servers. It gives finished points for managing information in transaction processing, enterprise intelligence and content administration applications.

Oracle Database 12c features

A chart on Oracle's web page indicates a complete listing of aspects with the aid of edition. Oracle besides offers a personal edition of Oracle Database 12 for individual clients. in addition, the company makes an categorical edition available as a free offering geared towards gaining knowledge of and simple, introductory usage. besides the fact that children, the specific edition is presently obtainable for less than Oracle Database 11g, the predecessor to 12c.

Oracle Database 12c provides more than 500 newfangled facets, together with a brand newfangled multi-tenant architecture that simplifies the system of consolidating databases in the cloud, enabling customers to manage many databases as one without altering their purposes. A unique multi-tenant container can host and control a entire lot of pluggable databases to dramatically in the reduction of expenses and simplify administration.

Oracle Database 12c additionally boasts a brand newfangled Database In-reminiscence column store, which speeds up database queries in order to allow companies to shape more advantageous and quicker selections. current applications can automatically and transparently engage potential of in-memory processing without requiring any adjustments or losing any current Oracle Database capabilities.

moreover, Oracle Database 12c presents more desirable protection features, enabling companies to present protection to their mission-critical company counsel internally and externally with facts redaction capabilities to eradicate sensitive software records and a "privilege analysis" feature to aid establish clients and functions with entry privileges within the database. via superior applied sciences for encryption, auditing and access handle, Oracle additionally gives a comprehensive set of defense-in-depth statistics protection tools that can hearten tackle regulatory compliance.

Oracle Database 12c versions

Oracle Database 12c is attainable basically in three variants designed to happy diverse purchasers' functionality needs and budgets:

  • Oracle Database traffic version offers unlimited CPU and reminiscence utilization (as much as the OS optimum), as well as the entire points of Oracle Database 12c, however just about 20 of them -- together with the Multitenant and Database In-memory applied sciences -- are optional add-ons that should be bought one at a time. list expenditures for the traffic version birth at $90 for a Named person Plus license or $forty seven,500 for a processor license.
  • Oracle Database universal edition is a lessen-can charge choice with a greater limited role set for miniature and medium-sized groups or departmental applications. The touchstone version will besides be licensed simplest on servers with a highest faculty of four sockets. If Named person Plus licensing is used, at the very least 5 licenses is required.
  • Oracle touchstone edition One is an entry-stage offering that may besides be used only on servers with up to two sockets. The other fundamental dissimilarity between it and the common version is that it does not support Oracle's trusty utility Clusters and automatic workload administration applied sciences.
  • Oracle Database 12c OSes

    Oracle Database 12c is a heterogeneous relational database administration paraphernalia that runs on windows, Linux and Unix working systems and is supported throughout a broad latitude of server and storage hardware methods. Supported OS models encompass:

  • Microsoft home windows x64
  • Linux x86-64 and zLinux64
  • Oracle Solaris (for both SPARC and x86 systems)
  • HP-UX Itanium
  • IBM AIX (PPC64)
  • zLinux sixty four
  • Oracle Database 12c licensing

    Oracle, the largest relational database management device seller, offers each perpetual and term licenses, the latter in annual increments from one to 5 years. The license metrics used for identifying the cost of Oracle Database 12c purchases are primarily based both on the number of named clients and devices that allows you to absorb access to the application -- officially referred to as Named consumer Plus -- or the variety of processors the database will flee on.

    Oracle Database 12c software licenses are bought at once by means of Oracle and thru resellers. Oracle uses a compund of written agreements to license its application. The overarching license rights are described in the Oracle master settlement, and the rights related to inescapable products and features are described within the Ordering document.

    Oracle Database 12c statistics varieties

    besides the just numeric, personality and date/time information varieties supported by pass of All relational database administration techniques, Oracle Database 12c helps numerous information varieties in addition to person-described varieties.

    Oracle's Exadata database appliance is additionally purchasable for clients looking for a less complicated, plug-and-play DBMS. Oracle offers diverse configurations in line with DBMS usage that encompass servers tailor-made for cumbersome online processing as well as facts warehouse functions.

    Oracle Database 12c benchmarks

    even though it's wise to conduct your own performance benchmarks, Oracle always benchmarks its DBMS and publishes the consequences. Many organizations absorb carried out lofty availability and performance using Oracle Database with their applications.

    in regards to the authorCraig S. Mullins is a data management strategist, researcher, consultant and creator with more than 30 years of event in All sides of database programs building. he's president and primary advisor of Mullins Consulting Inc. and publisher/editor of e mail him at

    e mail us at and comply with us on Twitter: @sDataManagement.

    Extracting Insights from Oracle Database | actual Questions and Pass4sure dumps

    Jul 7, 2017

    Stephanie Simone

    Extracting probably the most cost of your records through actual-time and advert hoc analytics isn’t All the time as easy because it sounds.

    Correlating facts to a lot of interior and exterior sources and making a bridge between structured and unstructured information outlets can besides be a challenge. however, it doesn’t absorb to be an issue with Oracle.

    contemporary information warehousing challenges consist of inflexibility, complicated structure, sluggish performance, faded know-how, and a want of governance, according to Axel Streichardt, senior director of enterprise applications options at sheer Storage, who discussed in a coincident webinar how sheer Storage's information platform can hearten shoppers seeking a lofty performance solution to shape the suffer to analytics efficient and positive.

    in line with Streichardt, sheer Storage’s Oracle retort can support enterprises modernize an Oracle data warehouse to bear perception from statistics at once. The tools and infrastructure to carry out this are an Oracle Database 11g or bigger, supported by pass of an all-flash storage answer, akin to sheer Storage FlashBlade, Streichardt  pointed out.

    The sheer FlashBlade array is an all-flash storage architecture. It connects without delay to Oracle databases by means of dNFS, and, with tremendous pools of study and write bandwidth.

    With FlashBlade companies accumulate scalable skill at a miniature kindhearted ingredient, faster database scan quotes, faster statistics masses (ETL), and lessen TCO with Oracle dNFS.

    the well-known thing cost propositions the solution gives to groups includes:

  • Jobs getting carried out quicker with consistent excessive performance
  • Scalability in IOPs, BW, low latency and quick deletes
  •  more skill/efficiency in less space
  • high potential in miniature 4U kindhearted aspect vastly reduces footprint
  •  fundamental to manage
  • handy HW deploy, efficient/computerized gadget startup
  •  Future proof architecture
  • New coincident inventive architecture
  •  Low Environmental working necessities
  • lessen vigour, lessen cooling
  •  business and Procurement model
  • sensible Storage – rapid in All places, effortless and Evergreen
  • An archived on-demand replay of this webinar is purchasable prerogative here.

    Oracle® Database 11g operating on solar SPARC enterprise M9000 Server sets World list TPC-H Three Terabyte Non-Clustered Benchmark result | actual Questions and Pass4sure dumps

    Oracle(R) Database 11g operating on solar SPARC commercial enterprise M9000 Server sets World list TPC-H Three Terabyte Non-Clustered Benchmark outcome

    supply: Oracle service provider

    Oracle Corporation

    October 14, 2010 08:00 ET

    Oracle Continues to bring imposing information Warehousing performance, Beats IBM

    REDWOOD SHORES, CA--(Marketwire - October 14, 2010) -  Oracle (NASDAQ: ORCL)

    news records

  • today, Oracle introduced an international list non-clustered TPC-H 3 TB benchmark influence demonstrating the sophisticated scalability of Oracle® Database 11g operating on Oracle's sun SPARC traffic M9000 server (1).
  • This upshot sets a newfangled checklist, outperforming the most effective IBM TPC-H three TB outcomes, providing 27 p.c greater performance with a 19 percent more suitable cost/performance (2).
  • Benchmark details

  • working on a sun SPARC traffic M9000 server, fitted with 32 SPARC64 VII 2.88 GHz processors, and solar Storage 6180 arrays, Oracle Database 11g with Oracle Solaris completed a global list TPC-H 3 TB non-clustered efficiency result of 198,907.5 QphH@3000GB with a cost of $16.58/QphH@3000GB. 
  • moreover, Oracle Database 11g running on the sun SPARC commercial enterprise M9000 server loaded the entire database 2.7 times faster than the IBM vigour 595 equipment(3) while keeping the optimum degree of storage redundancy.
  • helping quotes

  • "This benchmark outcome clearly demonstrates the sophisticated records warehousing efficiency and cost delivered via the combination of Oracle Database 11g and Oracle's sun SPARC commercial enterprise M9000 server," talked about Juan Loaiza, senior vp, systems know-how, Oracle. "This benchmark outcomes provides customers with an excellent metric to substantiate their investment in Oracle."
  • assisting materials

    About Oracle Oracle is the area's most comprehensive, open, and integrated enterprise utility and hardware systems company. For extra assistance about Oracle, delight consult with their net site at


    Oracle and Java are registered emblems of Oracle and/or its affiliates. different names may be trademarks of their respective owners.

    supply: Transaction Processing efficiency Council (TPC)

    As of October 6, 2010:

    (1) solar SPARC traffic M9000 server: 198,907.5QphH@3000GB, $16.58/QphH@3000GB, available 04/05/2011 (world list non-clustered TPC-H three TB outcomes). 

    (2) IBM power 595 model 9119-FHA, 156,537.3 QphH@3000GB, $20.60/QphH@3000GB, purchasable 11/24/2009. (most profitable IBM TPC-H 3000GB performance (QphH) and price/performance ($/QphH) influence).

    (3) sun SPARC enterprise M9000 server Database Load Time - 3:forty:11, IBM energy 595 Database Load Time - 10:02:25. 602 min /220 min = 2.7

    For a stronger adventure using this site, delight upgrade to a modern net browser.

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    Data Warehousing 11g Essentials

    Pass 4 sure 1Z0-515 dumps | 1Z0-515 actual questions |

    Performance Caching in a Snowflake Data Warehouse | actual questions and Pass4sure dumps

    Like any modern database, Snowflake uses various levels of caching to maximize query performance and reduce disk I/O and machine load. This article provides an overview of the techniques used by Snowflake, the data warehouse built for the cloud.

    System Architecture

    Before starting, it’s worth considering the underlying Snowflake architecture and explaining when Snowflake caches data. The diagram below illustrates the overall architecture, which consists of three layers:

    Image title

  • The Service Layer accepts SQL requests from users, coordinates queries, and manages transactions and results. Logically, this can be assumed to hold the result cache, a cached copy of the results of every query executed.
  • The Compute Layer actually does the cumbersome lifting. This is where the actual SQL is executed across the nodes of a Virtual Data Warehouse. This layer holds a cache of data queried, and is often referred to as Local Disk I/O, although in reality this is implemented using SSD storage. All data in the compute layer is temporary and only held as long as the virtual warehouse is active.
  • The Storage Layer provides long-term storage of results. This is often referred to as Remote Disk and is currently implemented on either Amazon S3 or Microsoft Blob storage.
  • Snowflake Caching Layers

    The diagram below illustrates the levels at which data and results are cached for subsequent use:

  • The Result Cache holds the results of every query executed in the past 24 hours. These are available across virtual warehouses, so query results returned to one user are available to every user in the system.
  • The Local Disk Cache caches data used in queries. Whenever data is needed for a given query, it's retrieved from the Remote Disk storage, and cached for subsequent queries here.
  • The Remote Disk holds the long-term storage. This level is liable for data resilience, which in the case of Amazon Web Services, means 99.999999999% durability, even in the event of an entire data hub failure.
  • Image title

    Benchmark Testing Setup

    Every Snowflake database is delivered with a pre-built and populated set of Transaction Processing Council (TPC) benchmark tables. To test the result of caching, I set up a chain of test queries against a miniature subset of the data, which is illustrated below.

    Image title

    All the queries were executed on a MEDIUM sized cluster (8 nodes) and joined the tables. The tables were queried exactly as is, without any performance tuning.

    The following query was executed multiple times, and the elapsed time and query diagram were recorded each time.

    Image title

    The screenshot below illustrates the results of the query which summarise the data by Region and Country. In total, the SQL queried, summarized, and counted 1.5 billion rows of data. The screenshot shows the first eight lines returned.

    Image title

    Test Sequence

    The test sequence was as follows:

  • Run from cold: Starting a newfangled virtual warehouse (with no local disk caching), and executing the query.
  • Run from warm: Disabling the result caching and repeating the query. This made exhaust of the local disk caching.
  • Run from hot: Again, repeated the query, but with the result caching switched on.
  • Each query included over 60Gb of raw data, although as Snowflake was able to automatically compress the data, the actual data transfers were around 12Gb. As Snowflake is a columnar data warehouse, it automatically returns the columns needed rather then the entire row to maximize query performance.

    Run From Cold

    This query returned in around 20 seconds and demonstrates it scanned around 12Gb of compressed data, with 0% from the local disk cache. This means it had no benefit from disk caching.

    Image title

    The bar chart above demonstrates around 50% of the time was spent on local or remote disk I/O, and only 2% on actually processing the data. Clearly any design changes they can carry out to reduce the disk I/O will hearten this query.

    The results besides demonstrate the queries were unable to effect any partition pruning which might better query performance. We’ll cover the upshot of partition pruning and clustering in the next article.

    Run From Warm

    This query was executed immediately after, but with the result cache disabled, and it completed in 1.2 seconds – around 16 times faster. In this case, the Local Disk cache (which is actually SSD on Amazon Web Services) was used to recur results, and disk I/O is no longer a concern.

    Image title

    In the above case, the disk I/O has been reduced to around 11% of the total elapsed time, and 99% of the data came from the (local disk) cache. While querying 1.5 billion rows, this is clearly an excellent result.

    Run From Hot

    This query returned results in milliseconds and involved re-executing the query, but with this time, the result cache enabled. Normally, this is the default situation, but it was disabled for testing purposes.

    Image title

    The above profile indicates the entire query was served directly from the result cache (taking around 2 milliseconds). Although not immediately obvious, many dashboard applications involve repeatedly refreshing a chain of screens and dashboards by re-executing the SQL. In these cases, the results are immediately returned in milliseconds.

    Although more information is available in the Snowflake Documentation, a chain of tests demonstrated the result cache will be reused unless the underlying data (or SQL query) has changed. As a chain of additional tests demonstrated inserts, updates and deletes which don't palpate the underlying data are ignored, and the result cache is used, provided data in the micro-partitions remains unchanged.

    Finally, results are normally retained for 24 hours, although the clock is reset every time the query is re-executed, up to a limit of 30 days, after which results query the remote disk.


    The sequence of tests was designed purely to illustrate the upshot of data caching on Snowflake. The tests included:-

  • Raw Data: Including over 1.5 billion rows of TPC generated data, a total of over 60Gb of raw data
  • Initial Query: Took 20 seconds to complete, and ran entirely from the remote disk.  Quite impressive.
  • Second Query: Was 16 times faster at 1.2 seconds and used the Local Disk (SSD) cache.
  • Result Set Query: Returned results in 130 milliseconds from the result cache (intentionally disabled on the prior query).  
  • To keep the above results in context, I repeatedly ran the very query on Oracle 11g production database server for a tier one investment bank and it took over 22 minutes to complete.

    Finally, unlike Oracle where additional charge and effort must be made to ensure revise partitioning, indexing, stats gathering and data compression, Snowflake caching is entirely automatic and available by default. Absolutely no effort was made to tune either the queries or the underlying design, although there are options available, which I'll discuss in the next article. badge up below for further details.

    Further Reading

    Thank you for reading this. If you found this article consuming and would infatuation to know more, you can read more about Snowflake, and Data Warehousing at

    One of the most Popular articles I’ve written describes my suffer as an Oracle warehouse architect and compares this to how Snowflake would maneuver the problems I encountered. You can read more at The pattern Data Warehouse Architecture.


    big data ,caching ,database ,performance ,performance optimization ,queries ,performance caching

    Integration between SAP Data Services, traffic Warehouse on the soar | actual questions and Pass4sure dumps

    Because I specialize in the world of SAP traffic Warehouse (BW), SAP Data Services was relatively foreign to me until a couple of years ago. But since the 4.0 release of Data Services, I've been intrigued by the concept of using it with BW and other SAP applications, allowing both systems to play to their strengths.

    That is to say, SAP Data Services provides excellent and user-friendly transformation capabilities and easy access to a plethora of viable data sources and targets. On the other hand, SAP BW provides a semantic data warehousing model, advanced hierarchy support, data lifecycle management services, security and more. The combination of Data Services with traditional SAP products infatuation BW could be a powerful solution for many, though there are still a couple of pitfalls to be aware of.

    Improvements in DS 4.0, BW 7.3

    Integration with SAP sources including BW took a broad jump with Data Services 4.0 and was further refined with the recent release of Data Services 4.1.

    The newest version of Data Services can read directly from SAP's "business content" extractors in the ERP, CRM and SRM applications. The table structure in these applications can be extremely complex. Extractors act as semantic views on top of these tables and can provide additional capabilities infatuation delta change capture. This functionality was introduced in Data Services 4.0, and parallel processing was improved in version 4.1.

    Data Services 4.0 besides supports autochthonous loading of BW DataSource objects, and Data Services is better integrated with BW as a Source System type. This newfangled integration makes it very easy to draw data from Data Services sources directly into BW DataSources, All from the BW interface. (DataSources is BW's data ingestion construct.) These capabilities are available in BW version 7.3 or later.

    SAP Data Services 4.1 besides introduces improved parallel loading when using BW's OpenHub tables as a source for Data Services. This is an well-known functionality for many customers because efficiently poignant data out of BW and into other systems for analysis and reporting can be a challenge.

    Advantages Data Services can present now

    These improvements result in a compelling case for using BW and Data Services together.

    SAP traffic Content extractors, originally developed for exhaust with BW, are the semantic representation of ERP data for analytic uses. Now they are available directly for non-BW exhaust via Data Services. These extractors should travail without interference even when simultaneously used for BW extraction, and can now be used to feed other data marts or an operational data store instead of, or in parallel with, BW loads.

    Data Services is attractive for current BW shops because it is much better at some types of transformations than BW. Pivot transformations, for example, are difficult in BW, often requiring custom-coded routines. In some environments, skills to develop involved transformations may be easier to arrive by when developing these transformations in Data Services' custom progress language (Python) than in ABAP. Doing custom transformation progress in Data Services, rather than BW, may shape custom progress more reusable, while reducing the complexity of the BW environment.

    Data Services besides enables a different software lifecycle management approach. BW changes are often limited to preplanned transport windows every week or month. This ensures a stable system but can be problematic for transformation logic that needs to closely track changes in source systems. Segregating source-system-oriented transformations in Data Services could allow a software lifecycle management regime with faster reactions to source-system changes or errors.

    BW still has advantages over traditional databases when used as a Data Services data store, and current Data Services shops may want to consider it for this reason. Some of the advantages are outlined in this article comparing the differences between BW and HANA.

    Still some uneven edges

    There are still some uneven edges in the Data Services integration with SAP applications and BW. Data Services support for traffic content extractors is extensive, but it’s limited to extractors for "Operational Data Provisioning," which doesn’t cover every scenario. You can find the full list of supported extractors in note 1558737 (login is required).

    The integration between BW Source Systems, BW DataSources, Data Services Targets and Data Services Jobs is besides confusing. When you create a Data Services source system in BW, you can only exhaust Data Services as a "dumb pipe" to load data to BW. There is no faculty to transform data by using Data Services in this scenario. This approach throws out much of Data Services' power in the designation of simplicity.

    However, All is not lost. If you configure the Data Services connection to BW, and then create a External System source system in BW (instead of a Data Services source system), you can create an arbitrarily structured BW DataSources and BW InfoPackages configuration to flee Data Services jobs of your choosing.

    If the Data Services job's target has the very structure as the BW DataSource, everything works. Surprisingly, the load will travail even if the target of the Data Services job appears to be a different DataSource. This is a nice functionality because it allows people to leverage any Data Services transformation logic directly from BW. But it's besides perilous because there are no restrictions forcing you to match structures, though the capability is likely to be found only by more audacious users.

    Figure 1: Different Source System types for Data Services behave differently in BW. “Data Services” source systems allow you to create DataSources that exactly mirror the structure of a Data Services “Source”. “External System” source systems allow you to create DataSources with arbitrary structures that are filled using any Data Services Job you choose.

    Data Services can now shape exhaust of a lot more of the functions that both SAP applications and BW absorb to offer. This should shape life a lot easier for those trying to exhaust Data Services in an environment with other SAP applications, and present some especially consuming newfangled options to BW shops. That said, this is a relatively newfangled functionality, so it suffers from bugs and implementation inconsistencies that can be confusing. Remaining aware of the shortcomings and hidden functionality can ease integration headaches.

    Oracle® Database 11g Running on Sun SPARC Enterprise M9000 Server Sets World Record TPC-H Three Terabyte Non-Clustered Benchmark Result | actual questions and Pass4sure dumps

    Oracle(R) Database 11g Running on Sun SPARC Enterprise M9000 Server Sets World Record TPC-H Three Terabyte Non-Clustered Benchmark Result

    SOURCE: Oracle Corporation

    Oracle Corporation

    October 14, 2010 08:00 ET

    Oracle Continues to Deliver Exceptional Data Warehousing Performance, Beats IBM

    REDWOOD SHORES, CA--(Marketwire - October 14, 2010) -  Oracle (NASDAQ: ORCL)

    News Facts

  • Today, Oracle announced a world record non-clustered TPC-H 3 TB benchmark result demonstrating the superior scalability of Oracle® Database 11g running on Oracle's Sun SPARC Enterprise M9000 server (1).
  • This result sets a newfangled record, outperforming the best IBM TPC-H 3 TB result, delivering 27 percent more performance with a 19 percent better price/performance (2).
  • Benchmark Details

  • Running on a Sun SPARC Enterprise M9000 server, equipped with 32 SPARC64 VII 2.88 GHz processors, and Sun Storage 6180 arrays, Oracle Database 11g with Oracle Solaris achieved a world record TPC-H 3 TB non-clustered performance result of 198,907.5 QphH@3000GB with a cost of $16.58/QphH@3000GB. 
  • Additionally, Oracle Database 11g running on the Sun SPARC Enterprise M9000 server loaded the entire database 2.7 times faster than the IBM Power 595 system(3) while maintaining the highest level of storage redundancy.
  • Supporting Quotes

  • "This benchmark result clearly demonstrates the superior data warehousing performance and value delivered by the combination of Oracle Database 11g and Oracle's Sun SPARC Enterprise M9000 server," said Juan Loaiza, senior vice president, Systems Technology, Oracle. "This benchmark result provides customers with a imposing metric to substantiate their investment in Oracle."
  • Supporting Resources

    About Oracle Oracle is the world's most complete, open, and integrated traffic software and hardware systems company. For more information about Oracle, delight visit their Web site at


    Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners.

    Source: Transaction Processing Performance Council (TPC)

    As of October 6, 2010:

    (1) Sun SPARC Enterprise M9000 server: 198,907.5QphH@3000GB, $16.58/QphH@3000GB, available 04/05/2011 (world record non-clustered TPC-H 3 TB result). 

    (2) IBM POWER 595 Model 9119-FHA, 156,537.3 QphH@3000GB, $20.60/QphH@3000GB, available 11/24/2009. (Best IBM TPC-H 3000GB performance (QphH) and price/performance ($/QphH) result).

    (3) Sun SPARC Enterprise M9000 server Database Load Time - 3:40:11, IBM POWER 595 Database Load Time - 10:02:25. 602 min /220 min = 2.7

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